Data scientist @ funda

Applied Statistics

thatssorandom.com

@edwin_thoen

CRAN: padr, GGally, recipes

Who:

Who:

• is very experienced in using NSE?

Who:

• is very experienced in using NSE?
• applies it now and then?

Who:

• is very experienced in using NSE?
• applies it now and then?
• only knows it at face value?

Who:

• is very experienced in using NSE?
• applies it now and then?
• only knows it at face value?
• is not really sure what it is?

We don't have to use R when using R!

mtcars$cyl_drat <- mtcars$cyl + mtcars$drat

We don't have to use R when using R!

mtcars$cyl_drat <- mtcars$cyl + mtcars$drat

Instead of this, we can do

library(dplyr)
mtcars <- mtcars %>% mutate(cyl_drat = cyl + drat)

or

mtcars_dt <- data.table::as.data.table(mtcars)
mtcars_dt[, cyl_drat := cyl + drat]

When you started using R, did you mix up?

install.packages("padr")

and

library(padr)

Apparantly, things that ought not to work, are working.

subset(mtcars, cyl == 6)

ggplot2::ggplot(mtcars, aes(mpg, drat)) +
  geom_point()

data.table::as.data.table(mtcars)[ ,mean(mpg), by = cyl]

• Because you are a geek and can't stand not knowing :)

• Because you are a geek and can't stand not knowing :)
• Because most R users slip into tool design sooner or later.

By creating a variable we bind a value to a name.

my_val <- 123

Binding happens in an environment, in this case the global.

By creating a variable we bind a value to a name.

my_val <- 123

Binding happens in an environment, in this case the global.

Just call my name, I'll give you the value:

my_val

## [1] 123

This is evaluating the variable name.

R starts looking for the value of name in the environment the name is called in.

x <- "a variable in the global"
a_func <- function() {
  x <- "a variable in the local"
  x
}
a_func()

## [1] "a variable in the local"

When it can't find it locally, move up to the parent environment (where the current env was created).

z <- "a variable in the global"
another_func <- function() {
  z
}
another_func()

## [1] "a variable in the global"

Error is thrown when the variable can't be found.

nobody_loves_me <- function() {
  y
}
nobody_loves_me()

## Error in nobody_loves_me(): object 'y' not found

So this is standard evaluation in R.

Postpone judgement, store variable name in a name object.

quote(wait_for_it)

## wait_for_it

quote(wait_for_it) %>% class()

## [1] "name"

Postpone judgement, store variable name in a name object.

quote(wait_for_it)

## wait_for_it

quote(wait_for_it) %>% class()

## [1] "name"

This is the act of quoting, saving a variable name to be evaluated later.

(name is also called symbol)

Quoted variable names not evaluated. It doesn't matter if they don't exist.

quoted_var <- quote(wait_for_it)
quoted_var

## wait_for_it

quoted_var %>% class()

## [1] "name"

Look for the value only when we ask to evaluate it.

eval(quoted_var)

## Error in eval(quoted_var): object 'wait_for_it' not found

wait_for_it <- "I finally have a value"
eval(quoted_var)

## [1] "I finally have a value"

We can evaluate the name in a different environment.

dplyr::pull(mtcars, cyl)

##  [1] 6 6 4 6 8 6 8 4 4 6 6 8 8 8 8 8 8 4 4 4 4 8 8 8 8 4 4 4 8 6 8 4

diy_pull <- function(x, name) {
  eval(name, envir = x)
}

diy_pull(mtcars, quote(cyl))

##  [1] 6 6 4 6 8 6 8 4 4 6 6 8 8 8 8 8 8 4 4 4 4 8 8 8 8 4 4 4 8 6 8 4

diy_pull <- function(x, name) {
  eval(name, envir = x)
}

diy_pull(mtcars, quote(cyl))

##  [1] 6 6 4 6 8 6 8 4 4 6 6 8 8 8 8 8 8 4 4 4 4 8 8 8 8 4 4 4 8 6 8 4

A data frame is an environment too. Column names act as variables.

mtcars %>% select(cyl)
as.data.table(mtcars)[, cyl]
ggplot(mtcars, aes(cyl)) + geom_bar()

Why does R not throw an error? There is no cyl in the global…

Would this work?

diy_pull <- function(x, bare_name) {
  name <- quote(bare_name)
  eval(name, env = x)
}

Would this work?

diy_pull_2 <- function(x, bare_name) {
  name <- quote(bare_name)
  eval(name, env = x)
}

diy_pull(mtcars, cyl)

## Error in eval(name, envir = x): object 'cyl' not found

substitute quotes the argument's content

substitute_example <- function(x) {
  substitute(x)
}
substitute_example(cyl)

## cyl

substitute_example(cyl) %>% class()

## [1] "name"

diy_pull <- function(x, bare_name) {
  name <- substitute(bare_name)
  eval(name, env = x)
}

diy_pull(mtcars, cyl)

##  [1] 6 6 4 6 8 6 8 4 4 6 6 8 8 8 8 8 8 4 4 4 4 8 8 8 8 4 4 4 8 6 8 4

koala <- function(x, y) {
  x + 42
}

koala(3)

## [1] 45

def koala(x, y):
  return(x + 42)
koala(3)

## TypeError: koala() takes exactly 2 arguments (1 given)
## 
## Detailed traceback: 
##   File "<string>", line 1, in <module>

We can quote the following things:

• name: the name of an R object

• call: calling of a function

• pairlist: something from the past you shouldn't bother about

• literal: evaluates to the value itself

Just like a name, a function call can be delayed by quoting.

my_little_filter <- function(x, 
                             call) {
  call_quoted <- substitute(call)
  retain_row  <- eval(call_quoted, envir = x)
  x[retain_row, ]
}

my_little_filter(mtcars, cyl == 4 & gear == 4) %>% head(2)

##    mpg cyl  disp hp drat   wt  qsec vs am gear carb cyl_drat
## 3 22.8   4 108.0 93 3.85 2.32 18.61  1  1    4    1     7.85
## 8 24.4   4 146.7 62 3.69 3.19 20.00  1  0    4    2     7.69

The value slot is empty at promise creation.

Only when the argument's expression is evaluated in the function, we start looking for it.

The value slot is empty at promise creation.

Only when the argument's expression is evaluated in the function, we start looking for it.

Remember koala?

koala <- function(x, y) {
  x + 42
}

When we call koala we create the following promise

x_value <- 42
koala(x = x_value)

That's how subsitute works!

Accesses the expression in the promise without evaluating it.

subs_func <- function(val) {
  vals_expr <- substitute(val)
  deparse(vals_expr)
}
subs_func(anything_goes)

## [1] "anything_goes"

Note that deparse coerces the expression to a character. Its inverse is parse.

The tidyverse NSE dialect.

mtcars %>% select(cyl)

We now know that cyl gets somehow quoted by select and evaluated within the data frame.

But what if we want to wrap tidyverse code in a custom function?

This won't work

my_tv_func <- function(x, grouping_var) {
  x %>% 
    group_by(grouping_var) %>% 
    summarise(max_drat = max(drat))
}
my_tv_func(mtcars, cyl)

Why?

In order to get it to work:

• quote the variable upfront
• unquote again before the argument is swallowed by the tidyverse function
• then tidyverse function can go back and quote it again

In order to get it to work:

• quote the variable upfront
• unquote again before the argument is swallowed by the tidyverse function
• then tidyverse function can go back and quote it again

my_tv_func <- function(x, grouping_var) {
  x %>% 
    group_by(!!grouping_var) %>% 
    summarise(max_drat = max(drat))
}
my_tv_func(mtcars, quo(cyl))

Just like using substitute you can quote the arguments value with enquo.

my_grouping_func <- function(x, grouping_var) {
  grouping_var_q <- enquo(grouping_var)
  x %>% 
    group_by(!!grouping_var_q) %>% 
    summarise(max_drat = max(drat))
}
my_grouping_func(mtcars, cyl)

## # A tibble: 3 x 2
##     cyl max_drat
##   <dbl>    <dbl>
## 1     4     4.93
## 2     6     3.92
## 3     8     4.22

my_filter <- function(x, bare_call) {
  call <- substitute(bare_call)
  x[eval(call, envir = x), ]
}

my_filter(mtcars, cyl == 4) %>% head(1)

##    mpg cyl disp hp drat   wt  qsec vs am gear carb cyl_drat
## 3 22.8   4  108 93 3.85 2.32 18.61  1  1    4    1     7.85

• cyl == 4 on itself is invalid, there is no cyl in the global.
• But, R refrains from judgement, stores it in a promise.
• substitute gets the expression, which is the quoted call.
• This expression is evaluated within the environment of x.
• Here it is completely valid, because there is a cyl column.

• quasiquotation

• quosures

• environments

edwinthoen@gmail.com

@edwin_thoen

github.com/EdwinTh/satRday

edwinth.github.io/blog/nse

edwinth.github.io/blog/dplyr-recipes